Overview

Dataset statistics

Number of variables67
Number of observations21251
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 MiB
Average record size in memory529.0 B

Variable types

Numeric5
Boolean1
Categorical61

Alerts

shelf_price is highly overall correlated with pct_disc and 1 other fieldsHigh correlation
pct_disc is highly overall correlated with shelf_price and 1 other fieldsHigh correlation
pct_retail_disc is highly overall correlated with shelf_price and 2 other fieldsHigh correlation
mailer_D is highly overall correlated with pct_retail_discHigh correlation
marital_status_A is highly overall correlated with hhsize_1High correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_1 and 3 other fieldsHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with kid_category_None/Unknown and 1 other fieldsHigh correlation
kid_category_1 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_4High correlation
kid_category_3+ is highly overall correlated with hhsize_5+High correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_2 Adults Kids and 4 other fieldsHigh correlation
hhsize_1 is highly overall correlated with marital_status_AHigh correlation
hhsize_2 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
hhsize_3 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
hhsize_4 is highly overall correlated with kid_category_2High correlation
hhsize_5+ is highly overall correlated with kid_category_3+High correlation
campaign_6.0 is highly overall correlated with description_TypeCHigh correlation
campaign_8.0 is highly overall correlated with description_TypeAHigh correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_18.0 is highly overall correlated with description_TypeAHigh correlation
campaign_29.0 is highly overall correlated with description_TypeBHigh correlation
description_TypeA is highly overall correlated with campaign_8.0 and 2 other fieldsHigh correlation
description_TypeB is highly overall correlated with campaign_29.0High correlation
description_TypeC is highly overall correlated with campaign_6.0High correlation
display_1 is highly imbalanced (99.3%)Imbalance
display_2 is highly imbalanced (91.2%)Imbalance
display_3 is highly imbalanced (93.5%)Imbalance
display_4 is highly imbalanced (98.8%)Imbalance
display_5 is highly imbalanced (99.3%)Imbalance
display_6 is highly imbalanced (95.1%)Imbalance
display_7 is highly imbalanced (90.6%)Imbalance
display_9 is highly imbalanced (96.2%)Imbalance
display_A is highly imbalanced (99.4%)Imbalance
mailer_C is highly imbalanced (98.6%)Imbalance
mailer_D is highly imbalanced (71.9%)Imbalance
mailer_H is highly imbalanced (93.1%)Imbalance
mailer_J is highly imbalanced (95.3%)Imbalance
homeowner_Probable Owner is highly imbalanced (88.7%)Imbalance
homeowner_Probable Renter is highly imbalanced (95.3%)Imbalance
homeowner_Renter is highly imbalanced (76.8%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (66.5%)Imbalance
kid_category_2 is highly imbalanced (53.6%)Imbalance
kid_category_3+ is highly imbalanced (52.4%)Imbalance
age_19-24 is highly imbalanced (74.5%)Imbalance
age_55-64 is highly imbalanced (64.5%)Imbalance
age_65+ is highly imbalanced (69.8%)Imbalance
income_100-124K is highly imbalanced (59.6%)Imbalance
income_125-149K is highly imbalanced (61.6%)Imbalance
income_15-24K is highly imbalanced (69.4%)Imbalance
income_150-174K is highly imbalanced (57.7%)Imbalance
income_175-199K is highly imbalanced (87.2%)Imbalance
income_200-249K is highly imbalanced (94.1%)Imbalance
income_25-34K is highly imbalanced (60.0%)Imbalance
income_250K+ is highly imbalanced (87.2%)Imbalance
income_Under 15K is highly imbalanced (70.5%)Imbalance
hhsize_4 is highly imbalanced (56.5%)Imbalance
hhsize_5+ is highly imbalanced (53.7%)Imbalance
campaign_6.0 is highly imbalanced (99.6%)Imbalance
campaign_8.0 is highly imbalanced (61.1%)Imbalance
campaign_13.0 is highly imbalanced (60.5%)Imbalance
campaign_18.0 is highly imbalanced (61.3%)Imbalance
campaign_29.0 is highly imbalanced (94.1%)Imbalance
campaign_30.0 is highly imbalanced (99.8%)Imbalance
description_TypeB is highly imbalanced (94.1%)Imbalance
description_TypeC is highly imbalanced (99.6%)Imbalance
Unnamed: 0 has unique valuesUnique
pct_disc has 7551 (35.5%) zerosZeros
pct_retail_disc has 7567 (35.6%) zerosZeros
pct_coupon_disc has 20956 (98.6%) zerosZeros

Reproduction

Analysis started2023-07-09 07:41:35.259847
Analysis finished2023-07-09 07:42:12.076213
Duration36.82 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct21251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17181.748
Minimum16
Maximum34388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-07-09T09:42:12.348113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile1846.5
Q18601.5
median16836
Q325946.5
95-th percentile32358.5
Maximum34388
Range34372
Interquartile range (IQR)17345

Descriptive statistics

Standard deviation9874.5023
Coefficient of variation (CV)0.57470884
Kurtosis-1.2337394
Mean17181.748
Median Absolute Deviation (MAD)8673
Skewness0.023473041
Sum3.6512932 × 108
Variance97505796
MonotonicityStrictly increasing
2023-07-09T09:42:12.624723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1
 
< 0.1%
23171 1
 
< 0.1%
23169 1
 
< 0.1%
23168 1
 
< 0.1%
23167 1
 
< 0.1%
23166 1
 
< 0.1%
23165 1
 
< 0.1%
23164 1
 
< 0.1%
23163 1
 
< 0.1%
23162 1
 
< 0.1%
Other values (21241) 21241
> 99.9%
ValueCountFrequency (%)
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
34388 1
< 0.1%
34387 1
< 0.1%
34386 1
< 0.1%
34385 1
< 0.1%
34384 1
< 0.1%
34383 1
< 0.1%
34382 1
< 0.1%
34381 1
< 0.1%
34380 1
< 0.1%
34379 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
False
14083 
True
7168 
ValueCountFrequency (%)
False 14083
66.3%
True 7168
33.7%
2023-07-09T09:42:12.856721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct74
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.156933
Minimum0.35
Maximum6.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-07-09T09:42:13.034608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile0.39
Q10.4
median0.79
Q30.79
95-th percentile3.59
Maximum6.59
Range6.24
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation1.1046807
Coefficient of variation (CV)0.95483548
Kurtosis3.6136688
Mean1.156933
Median Absolute Deviation (MAD)0.39
Skewness1.9435785
Sum24585.983
Variance1.2203194
MonotonicityNot monotonic
2023-07-09T09:42:13.235021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79 8494
40.0%
0.4 4451
20.9%
0.39 2120
 
10.0%
2.79 946
 
4.5%
0.4 577
 
2.7%
2.49 528
 
2.5%
2.99 434
 
2.0%
3.59 409
 
1.9%
1.49 389
 
1.8%
0.39 311
 
1.5%
Other values (64) 2592
 
12.2%
ValueCountFrequency (%)
0.35 1
 
< 0.1%
0.39 311
 
1.5%
0.39 2120
10.0%
0.4 577
 
2.7%
0.4 4451
20.9%
0.4 11
 
0.1%
0.44 1
 
< 0.1%
0.48 17
 
0.1%
0.5 8
 
< 0.1%
0.59 15
 
0.1%
ValueCountFrequency (%)
6.59 15
 
0.1%
5.79 245
1.2%
4.99 60
 
0.3%
4.89 2
 
< 0.1%
4.89 32
 
0.2%
4.49 56
 
0.3%
4.47 2
 
< 0.1%
4.47 3
 
< 0.1%
4.18 17
 
0.1%
4.18 3
 
< 0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct227
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15831646
Minimum0
Maximum0.90909091
Zeros7551
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-07-09T09:42:13.454836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.16666667
Q30.24050633
95-th percentile0.36708861
Maximum0.90909091
Range0.90909091
Interquartile range (IQR)0.24050633

Descriptive statistics

Standard deviation0.14584127
Coefficient of variation (CV)0.92120091
Kurtosis0.19144625
Mean0.15831646
Median Absolute Deviation (MAD)0.14594128
Skewness0.59268828
Sum3364.3831
Variance0.021269675
MonotonicityNot monotonic
2023-07-09T09:42:13.684741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7551
35.5%
0.2405063291 5743
27.0%
0.3670886076 1628
 
7.7%
0.1039426523 662
 
3.1%
0.4936708861 525
 
2.5%
0.3288590604 377
 
1.8%
0.08032128514 333
 
1.6%
0.175 290
 
1.4%
0.1003344482 248
 
1.2%
0.2228412256 236
 
1.1%
Other values (217) 3658
17.2%
ValueCountFrequency (%)
0 7551
35.5%
0.01497005988 15
 
0.1%
0.02044989775 1
 
< 0.1%
0.04545454545 17
 
0.1%
0.05527638191 38
 
0.2%
0.05974842767 1
 
< 0.1%
0.06147540984 1
 
< 0.1%
0.06688963211 1
 
< 0.1%
0.07063197026 11
 
0.1%
0.07434944238 17
 
0.1%
ValueCountFrequency (%)
0.9090909091 1
 
< 0.1%
0.8938547486 1
 
< 0.1%
0.8734177215 25
0.1%
0.7890295359 3
 
< 0.1%
0.7751937984 1
 
< 0.1%
0.746835443 12
0.1%
0.72899729 1
 
< 0.1%
0.721448468 3
 
< 0.1%
0.719665272 2
 
< 0.1%
0.7046413502 3
 
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct143
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15332965
Minimum-0
Maximum0.70234114
Zeros7567
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-07-09T09:42:14.054778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median0.16317992
Q30.24050633
95-th percentile0.36708861
Maximum0.70234114
Range0.70234114
Interquartile range (IQR)0.24050633

Descriptive statistics

Standard deviation0.13754279
Coefficient of variation (CV)0.89703976
Kurtosis-0.88070498
Mean0.15332965
Median Absolute Deviation (MAD)0.13742129
Skewness0.33565764
Sum3258.4084
Variance0.01891802
MonotonicityNot monotonic
2023-07-09T09:42:14.265038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 7567
35.6%
0.2405063291 5808
27.3%
0.3670886076 1642
 
7.7%
0.1039426523 697
 
3.3%
0.4936708861 487
 
2.3%
0.3288590604 375
 
1.8%
0.08032128514 353
 
1.7%
0.175 305
 
1.4%
0.2228412256 251
 
1.2%
0.1003344482 251
 
1.2%
Other values (133) 3515
16.5%
ValueCountFrequency (%)
-0 7567
35.6%
0.01497005988 15
 
0.1%
0.02044989775 1
 
< 0.1%
0.04545454545 17
 
0.1%
0.05527638191 38
 
0.2%
0.05974842767 1
 
< 0.1%
0.06147540984 1
 
< 0.1%
0.06688963211 1
 
< 0.1%
0.07063197026 12
 
0.1%
0.07434944238 17
 
0.1%
ValueCountFrequency (%)
0.7023411371 1
 
< 0.1%
0.6711409396 1
 
< 0.1%
0.4936708861 487
2.3%
0.4936708861 39
 
0.2%
0.4285714286 13
 
0.1%
0.4202898551 4
 
< 0.1%
0.4178272981 1
 
< 0.1%
0.3920972644 2
 
< 0.1%
0.373433584 4
 
< 0.1%
0.3717472119 1
 
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0049868088
Minimum-0
Maximum0.90909091
Zeros20956
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size166.1 KiB
2023-07-09T09:42:14.444931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median0
Q3-0
95-th percentile-0
Maximum0.90909091
Range0.90909091
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.044609877
Coefficient of variation (CV)8.945576
Kurtosis105.95238
Mean0.0049868088
Median Absolute Deviation (MAD)0
Skewness9.863123
Sum105.97467
Variance0.0019900411
MonotonicityNot monotonic
2023-07-09T09:42:14.584960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 20956
98.6%
0.3584229391 42
 
0.2%
0.278551532 22
 
0.1%
0.6329113924 19
 
0.1%
0.5063291139 18
 
0.1%
0.27100271 17
 
0.1%
0.2949852507 14
 
0.1%
0.4016064257 14
 
0.1%
0.1792114695 13
 
0.1%
0.3039513678 12
 
0.1%
Other values (49) 124
 
0.6%
ValueCountFrequency (%)
-0 20956
98.6%
0.08130081301 1
 
< 0.1%
0.1253132832 1
 
< 0.1%
0.135501355 3
 
< 0.1%
0.1381692573 1
 
< 0.1%
0.1394052045 1
 
< 0.1%
0.1474926254 1
 
< 0.1%
0.1517450683 2
 
< 0.1%
0.1519756839 1
 
< 0.1%
0.1522070015 1
 
< 0.1%
ValueCountFrequency (%)
0.9090909091 1
 
< 0.1%
0.7751937984 1
 
< 0.1%
0.6329113924 1
 
< 0.1%
0.6329113924 19
0.1%
0.5917159763 2
 
< 0.1%
0.5586592179 1
 
< 0.1%
0.5063291139 18
0.1%
0.5025125628 1
 
< 0.1%
0.5020080321 2
 
< 0.1%
0.4587155963 1
 
< 0.1%

display_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21239 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Length

2023-07-09T09:42:14.707045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:14.850000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21239
99.9%
1 12
 
0.1%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21014 
1
 
237

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Length

2023-07-09T09:42:14.965006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:15.095104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21014
98.9%
1 237
 
1.1%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21089 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Length

2023-07-09T09:42:15.191594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:15.297188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21089
99.2%
1 162
 
0.8%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21228 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Length

2023-07-09T09:42:15.405043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:15.514730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21228
99.9%
1 23
 
0.1%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21238 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Length

2023-07-09T09:42:15.604994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:15.704643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21238
99.9%
1 13
 
0.1%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21135 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Length

2023-07-09T09:42:15.795115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:15.905007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21135
99.5%
1 116
 
0.5%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20995 
1
 
256

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Length

2023-07-09T09:42:15.994741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:16.094682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20995
98.8%
1 256
 
1.2%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21165 
1
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Length

2023-07-09T09:42:16.184700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:16.295064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21165
99.6%
1 86
 
0.4%

display_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21240 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Length

2023-07-09T09:42:16.380977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:16.484686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21240
99.9%
1 11
 
0.1%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18091 
1
3160 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Length

2023-07-09T09:42:16.564720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:16.674668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18091
85.1%
1 3160
 
14.9%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21225 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Length

2023-07-09T09:42:16.764628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:16.865103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21225
99.9%
1 26
 
0.1%

mailer_D
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20215 
1
 
1036

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Length

2023-07-09T09:42:16.954986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:17.055050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20215
95.1%
1 1036
 
4.9%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21076 
1
 
175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Length

2023-07-09T09:42:17.145114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:17.245101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21076
99.2%
1 175
 
0.8%

mailer_J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21139 
1
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Length

2023-07-09T09:42:17.335088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:17.435086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21139
99.5%
1 112
 
0.5%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
10861 
1
10390 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Length

2023-07-09T09:42:17.514724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:17.614710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring characters

ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10861
51.1%
1 10390
48.9%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18586 
1
2665 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Length

2023-07-09T09:42:17.704603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:17.804669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18586
87.5%
1 2665
 
12.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
1
14585 
0
6666 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Length

2023-07-09T09:42:17.894696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:17.994659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring characters

ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14585
68.6%
0 6666
31.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20929 
1
 
322

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Length

2023-07-09T09:42:18.085118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:18.185002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20929
98.5%
1 322
 
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21141 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Length

2023-07-09T09:42:18.277079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:18.580655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21141
99.5%
1 110
 
0.5%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20447 
1
 
804

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Length

2023-07-09T09:42:18.665016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:18.756517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20447
96.2%
1 804
 
3.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19934 
1
 
1317

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Length

2023-07-09T09:42:18.836299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:18.947180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19934
93.8%
1 1317
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
14980 
1
6271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Length

2023-07-09T09:42:19.026923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:19.125027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring characters

ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14980
70.5%
1 6271
29.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
14578 
1
6673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Length

2023-07-09T09:42:19.216306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:19.315036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring characters

ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14578
68.6%
1 6673
31.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18112 
1
3139 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Length

2023-07-09T09:42:19.406612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:19.504654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18112
85.2%
1 3139
 
14.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18644 
1
2607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Length

2023-07-09T09:42:19.595111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:19.694670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18644
87.7%
1 2607
 
12.3%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
17867 
1
3384 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Length

2023-07-09T09:42:19.784734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:19.905023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17867
84.1%
1 3384
 
15.9%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19157 
1
2094 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Length

2023-07-09T09:42:20.044641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:20.154602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19157
90.1%
1 2094
 
9.9%

kid_category_3+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19078 
1
2173 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Length

2023-07-09T09:42:20.249474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:20.360068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19078
89.8%
1 2173
 
10.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
1
13600 
0
7651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Length

2023-07-09T09:42:20.449688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:20.549727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring characters

ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13600
64.0%
0 7651
36.0%

age_19-24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20343 
1
 
908

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20343
95.7%
1 908
 
4.3%

Length

2023-07-09T09:42:20.634660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:20.741039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20343
95.7%
1 908
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 20343
95.7%
1 908
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20343
95.7%
1 908
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20343
95.7%
1 908
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20343
95.7%
1 908
 
4.3%

age_25-34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
16748 
1
4503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16748
78.8%
1 4503
 
21.2%

Length

2023-07-09T09:42:20.824656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:20.925070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 16748
78.8%
1 4503
 
21.2%

Most occurring characters

ValueCountFrequency (%)
0 16748
78.8%
1 4503
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16748
78.8%
1 4503
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16748
78.8%
1 4503
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16748
78.8%
1 4503
 
21.2%

age_35-44
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
15365 
1
5886 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15365
72.3%
1 5886
 
27.7%

Length

2023-07-09T09:42:21.014660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:21.114628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15365
72.3%
1 5886
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0 15365
72.3%
1 5886
 
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15365
72.3%
1 5886
 
27.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15365
72.3%
1 5886
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15365
72.3%
1 5886
 
27.7%

age_45-54
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
13862 
1
7389 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 13862
65.2%
1 7389
34.8%

Length

2023-07-09T09:42:21.205028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:21.305015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13862
65.2%
1 7389
34.8%

Most occurring characters

ValueCountFrequency (%)
0 13862
65.2%
1 7389
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13862
65.2%
1 7389
34.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13862
65.2%
1 7389
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13862
65.2%
1 7389
34.8%

age_55-64
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19827 
1
 
1424

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19827
93.3%
1 1424
 
6.7%

Length

2023-07-09T09:42:21.397142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:21.494692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19827
93.3%
1 1424
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 19827
93.3%
1 1424
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19827
93.3%
1 1424
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19827
93.3%
1 1424
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19827
93.3%
1 1424
 
6.7%

age_65+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20110 
1
 
1141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20110
94.6%
1 1141
 
5.4%

Length

2023-07-09T09:42:21.575016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:21.685032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20110
94.6%
1 1141
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 20110
94.6%
1 1141
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20110
94.6%
1 1141
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20110
94.6%
1 1141
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20110
94.6%
1 1141
 
5.4%

income_100-124K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19542 
1
 
1709

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19542
92.0%
1 1709
 
8.0%

Length

2023-07-09T09:42:21.765053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:21.864651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19542
92.0%
1 1709
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 19542
92.0%
1 1709
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19542
92.0%
1 1709
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19542
92.0%
1 1709
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19542
92.0%
1 1709
 
8.0%

income_125-149K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19661 
1
 
1590

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19661
92.5%
1 1590
 
7.5%

Length

2023-07-09T09:42:21.955050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:22.060103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19661
92.5%
1 1590
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 19661
92.5%
1 1590
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19661
92.5%
1 1590
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19661
92.5%
1 1590
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19661
92.5%
1 1590
 
7.5%

income_15-24K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20091 
1
 
1160

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20091
94.5%
1 1160
 
5.5%

Length

2023-07-09T09:42:22.145068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:22.244973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20091
94.5%
1 1160
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 20091
94.5%
1 1160
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20091
94.5%
1 1160
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20091
94.5%
1 1160
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20091
94.5%
1 1160
 
5.5%

income_150-174K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19424 
1
 
1827

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19424
91.4%
1 1827
 
8.6%

Length

2023-07-09T09:42:22.325033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:22.434975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19424
91.4%
1 1827
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 19424
91.4%
1 1827
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19424
91.4%
1 1827
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19424
91.4%
1 1827
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19424
91.4%
1 1827
 
8.6%

income_175-199K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20876 
1
 
375

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Length

2023-07-09T09:42:22.525059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:22.656941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

income_200-249K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21105 
1
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21105
99.3%
1 146
 
0.7%

Length

2023-07-09T09:42:22.794846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:22.954565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21105
99.3%
1 146
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 21105
99.3%
1 146
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21105
99.3%
1 146
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21105
99.3%
1 146
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21105
99.3%
1 146
 
0.7%

income_25-34K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19564 
1
 
1687

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19564
92.1%
1 1687
 
7.9%

Length

2023-07-09T09:42:23.136512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:23.334611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19564
92.1%
1 1687
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 19564
92.1%
1 1687
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19564
92.1%
1 1687
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19564
92.1%
1 1687
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19564
92.1%
1 1687
 
7.9%

income_250K+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20876 
1
 
375

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Length

2023-07-09T09:42:23.505014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:23.739547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20876
98.2%
1 375
 
1.8%

income_35-49K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
17424 
1
3827 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17424
82.0%
1 3827
 
18.0%

Length

2023-07-09T09:42:23.936825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:24.194598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17424
82.0%
1 3827
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 17424
82.0%
1 3827
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17424
82.0%
1 3827
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17424
82.0%
1 3827
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17424
82.0%
1 3827
 
18.0%

income_50-74K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
16490 
1
4761 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 16490
77.6%
1 4761
 
22.4%

Length

2023-07-09T09:42:24.434891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:24.709578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 16490
77.6%
1 4761
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 16490
77.6%
1 4761
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16490
77.6%
1 4761
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16490
77.6%
1 4761
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16490
77.6%
1 4761
 
22.4%

income_75-99K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
18565 
1
2686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18565
87.4%
1 2686
 
12.6%

Length

2023-07-09T09:42:24.959844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:25.594685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 18565
87.4%
1 2686
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 18565
87.4%
1 2686
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18565
87.4%
1 2686
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18565
87.4%
1 2686
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18565
87.4%
1 2686
 
12.6%

income_Under 15K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
20143 
1
 
1108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20143
94.8%
1 1108
 
5.2%

Length

2023-07-09T09:42:25.768678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:25.964694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 20143
94.8%
1 1108
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 20143
94.8%
1 1108
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20143
94.8%
1 1108
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20143
94.8%
1 1108
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20143
94.8%
1 1108
 
5.2%

hhsize_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
15280 
1
5971 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15280
71.9%
1 5971
 
28.1%

Length

2023-07-09T09:42:26.094927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:26.278413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15280
71.9%
1 5971
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 15280
71.9%
1 5971
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15280
71.9%
1 5971
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15280
71.9%
1 5971
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15280
71.9%
1 5971
 
28.1%

hhsize_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
13293 
1
7958 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 13293
62.6%
1 7958
37.4%

Length

2023-07-09T09:42:26.424922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:26.584981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13293
62.6%
1 7958
37.4%

Most occurring characters

ValueCountFrequency (%)
0 13293
62.6%
1 7958
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13293
62.6%
1 7958
37.4%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13293
62.6%
1 7958
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13293
62.6%
1 7958
37.4%

hhsize_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
17920 
1
3331 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17920
84.3%
1 3331
 
15.7%

Length

2023-07-09T09:42:26.724989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:26.879633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 17920
84.3%
1 3331
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 17920
84.3%
1 3331
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17920
84.3%
1 3331
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17920
84.3%
1 3331
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17920
84.3%
1 3331
 
15.7%

hhsize_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19347 
1
 
1904

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19347
91.0%
1 1904
 
9.0%

Length

2023-07-09T09:42:27.024640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:27.186084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19347
91.0%
1 1904
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 19347
91.0%
1 1904
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19347
91.0%
1 1904
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19347
91.0%
1 1904
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19347
91.0%
1 1904
 
9.0%

hhsize_5+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19164 
1
2087 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19164
90.2%
1 2087
 
9.8%

Length

2023-07-09T09:42:27.334725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:27.474662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19164
90.2%
1 2087
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 19164
90.2%
1 2087
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19164
90.2%
1 2087
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19164
90.2%
1 2087
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19164
90.2%
1 2087
 
9.8%

campaign_6.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21244 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Length

2023-07-09T09:42:27.604991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:27.814669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

campaign_8.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19628 
1
 
1623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Length

2023-07-09T09:42:28.014793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:28.206637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19628
92.4%
1 1623
 
7.6%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19596 
1
 
1655

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Length

2023-07-09T09:42:28.339828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:28.496531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19596
92.2%
1 1655
 
7.8%

campaign_18.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
19639 
1
 
1612

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Length

2023-07-09T09:42:28.624746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:28.797045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19639
92.4%
1 1612
 
7.6%

campaign_29.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21107 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Length

2023-07-09T09:42:28.914883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:29.164979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21248 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Length

2023-07-09T09:42:29.334660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:29.544948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21248
> 99.9%
1 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
16358 
1
4893 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Length

2023-07-09T09:42:29.744919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:29.957017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16358
77.0%
1 4893
 
23.0%

description_TypeB
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21107 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Length

2023-07-09T09:42:30.134527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:30.297991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21107
99.3%
1 144
 
0.7%

description_TypeC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.1 KiB
0
21244 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21251
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Length

2023-07-09T09:42:30.424692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T09:42:30.625587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21251
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21251
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21244
> 99.9%
1 7
 
< 0.1%

Interactions

2023-07-09T09:42:07.754665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:04.554647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.304664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.974720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:06.815025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:07.957581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:04.720837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.456117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:06.235141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:07.000964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:08.148593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:04.854659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.585567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:06.354955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:07.192582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:08.308912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.024884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.710993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:06.477135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:07.384727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:08.509807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.174711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:05.844749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:06.635996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-09T09:42:07.574918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-07-09T09:42:30.964916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discfirst_purchasedisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Hmailer_Jmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_6.0campaign_8.0campaign_13.0campaign_18.0campaign_29.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
Unnamed: 01.000-0.037-0.021-0.0240.0150.0590.0020.0250.0370.0270.0350.0470.0180.0260.0230.0350.0190.0430.0460.0140.1880.1300.1880.3040.1000.1720.1360.1640.1310.1610.1910.1740.1850.1880.1620.1480.2400.2190.1990.1610.1790.1980.1860.1210.2490.1980.1400.1530.1460.1970.1670.1000.1880.1560.0980.1910.1840.1870.0300.0500.0230.0600.0750.0000.0400.0750.030
shelf_price-0.0371.0000.5230.5100.1290.1180.0490.0410.2890.0310.0000.0380.0470.2250.0090.1300.1790.1270.0270.0360.0960.0450.1040.0450.0860.0680.1180.0690.1020.0750.1040.0470.1020.1700.1170.0530.0300.0520.0850.0730.1040.1010.1410.1230.1380.0890.0310.0530.0360.0480.0400.0270.1140.1050.0860.0470.0970.1750.0510.0450.0210.0670.0420.0670.0360.0420.051
pct_disc-0.0210.5231.0000.9840.2010.0640.0540.0790.1850.0240.0580.0990.1500.1420.0000.4350.1260.4640.2320.0760.0550.0510.0720.0760.0440.0370.1040.1030.0990.0840.0840.0570.0720.0940.1350.0510.0570.0490.0680.0350.0480.0960.0820.0930.0460.0300.0710.0370.0320.0590.0980.0430.0350.0800.0660.0530.0620.1050.0480.0980.0690.0720.0990.0000.0940.0990.048
pct_retail_disc-0.0240.5100.9841.0000.0420.0830.0500.1190.1890.0260.0320.0980.1600.1440.0000.4690.1360.5440.2350.0820.0540.0560.0800.0780.0490.0330.1180.0980.1000.0800.0870.0540.0670.1070.1350.0630.0540.0390.0580.0430.0490.1100.0980.0930.0530.0800.0900.0400.0400.0520.0920.0790.0280.0840.0630.0440.0590.1160.0460.1040.0720.0790.1000.0000.1120.1000.046
pct_coupon_disc0.0150.1290.2010.0421.0000.0330.0170.0140.0190.0000.0540.0180.0000.0150.0000.0200.0340.0110.0000.0000.0550.0220.0350.0000.0360.0100.0110.0450.0180.0170.0290.0090.0130.0810.0500.0000.0140.0240.0130.0000.0000.0170.0290.0000.0220.0190.0380.0000.0000.0090.0270.0110.0250.0360.0180.0060.0150.0840.0620.0170.0260.0100.0000.0000.0010.0000.062
first_purchase0.0590.1180.0640.0830.0331.0000.0000.0000.0180.0000.0000.0000.0280.0000.0000.0080.0040.0160.0140.0000.0510.0370.0550.0240.0260.0410.0220.0000.0300.0260.0360.0060.0150.0200.0150.0410.0330.0130.0000.0000.0070.1220.0260.0440.0620.0270.0100.0380.0030.0840.0000.0110.0400.0070.0180.0060.0080.0160.0100.0300.0280.0390.0000.0100.0620.0000.010
display_10.0020.0490.0540.0500.0170.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0660.0110.0000.0140.0000.0000.0000.0000.0000.0070.0000.0230.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0150.0120.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_20.0250.0410.0790.1190.0140.0000.0001.0000.0000.0000.0000.0000.0070.0000.0000.0860.0000.1080.0270.0190.0150.0000.0000.0000.0000.0080.0090.0000.0140.0110.0000.0000.0000.0180.0000.0100.0000.0000.0000.0120.0080.0030.0000.0000.0040.0000.0000.0000.0000.0220.0100.0120.0040.0000.0040.0080.0000.0180.0000.0100.0000.0160.0000.0000.0030.0000.000
display_30.0370.2890.1850.1890.0190.0180.0000.0001.0000.0000.0000.0000.0020.0000.0000.0240.0000.0170.0000.0000.0000.0000.0030.0000.0000.0340.0250.0160.0250.0010.0210.0000.0460.0000.0290.0160.0080.0030.0110.0190.0000.0000.0160.0000.0000.0000.0000.0000.0080.0000.0220.0070.0000.0020.0100.0080.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_40.0270.0310.0240.0260.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0040.0000.0070.0000.0030.0000.0000.0020.0130.0000.0150.0100.0050.0000.0090.0000.0050.0070.0000.0090.0000.0000.0070.0000.0270.0000.0000.0010.0000.0120.0000.0000.0000.0180.0000.0100.0040.0000.0000.0000.0010.0000.0000.0000.0000.0000.000
display_50.0350.0000.0580.0320.0540.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0170.0080.0170.0000.0090.0000.0000.0120.0080.0030.0150.0040.0000.0000.0150.0000.0000.0060.0230.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0060.0150.0000.0000.0280.0060.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_60.0470.0380.0990.0980.0180.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0700.0000.0310.0170.0000.0040.0300.0150.0000.0000.0000.0070.0000.0050.0120.0210.0000.0000.0000.0050.0290.0000.0040.0000.0050.0000.0000.0120.0080.0160.0450.0000.0120.0700.0020.0000.0040.0070.0000.0000.0000.0000.0000.0000.0140.0340.0160.0000.0000.0200.0000.000
display_70.0180.0470.1500.1600.0000.0280.0000.0070.0020.0000.0000.0001.0000.0000.0000.0790.0130.1140.0590.0000.0060.0160.0000.0000.0000.0050.0000.0000.0130.0080.0000.0090.0090.0000.0000.0000.0240.0100.0100.0110.0250.0000.0000.0350.0160.0110.0000.0060.0000.0050.0130.0080.0070.0000.0000.0000.0070.0000.0000.0000.0120.0200.0000.0000.0000.0000.000
display_90.0260.2250.1420.1440.0150.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0110.0000.0110.0000.0000.0000.0080.0020.0000.0000.0230.0180.0200.0250.0000.0060.0270.0130.0000.0290.0080.0080.0130.0000.0140.0080.0100.0130.0080.0080.0000.0000.0100.0560.0000.0140.0030.0110.0000.0130.0000.0160.0000.0000.0000.0000.0090.0000.0000.0000.0000.000
display_A0.0230.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0130.0010.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.000
mailer_A0.0350.1300.4350.4690.0200.0080.0000.0860.0240.0190.0000.0700.0790.0110.0001.0000.0110.0940.0370.0290.0160.0000.0270.0000.0000.0030.0000.0000.0000.0150.0040.0210.0160.0040.0000.0080.0000.0090.0000.0000.0080.0080.0030.0070.0190.0090.0150.0400.0100.0070.0140.0110.0000.0090.0110.0240.0090.0140.0000.0240.0760.0250.0000.0000.0140.0000.000
mailer_C0.0190.1790.1260.1360.0340.0040.0000.0000.0000.0000.0000.0000.0130.0000.0000.0111.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0120.0120.0000.0000.0100.0210.0000.0190.0000.0070.0000.0020.0000.0000.0000.0040.0000.0000.0000.0000.0100.0000.0000.0030.0000.0000.0000.0160.0190.0130.0000.0000.0030.0030.0170.0000.0000.0000.0000.000
mailer_D0.0430.1270.4640.5440.0110.0160.0000.1080.0170.0000.0150.0310.1140.0110.0000.0940.0001.0000.0180.0130.0000.0050.0000.0000.0000.0200.0000.0000.0150.0000.0020.0060.0090.0110.0040.0290.0180.0000.0050.0320.0000.0000.0000.0410.0000.0170.0070.0060.0000.0160.0330.0000.0000.0140.0250.0110.0000.0070.0000.0010.0050.0640.0160.0000.0410.0160.000
mailer_H0.0460.0270.2320.2350.0000.0140.0060.0270.0000.0000.0000.0170.0590.0000.0000.0370.0000.0181.0000.0000.0120.0050.0030.0040.0000.0150.0140.0000.0110.0040.0070.0130.0180.0110.0020.0000.0110.0060.0170.0000.0000.0080.0000.0290.0100.0000.0000.0150.0000.0000.0070.0230.0000.0000.0000.0120.0200.0090.0000.1220.0250.0000.0000.0000.0550.0000.000
mailer_J0.0140.0360.0760.0820.0000.0000.0660.0190.0000.0000.0000.0000.0000.0000.0000.0290.0000.0130.0001.0000.0030.0000.0000.0070.0000.0060.0100.0120.0060.0060.0000.0030.0000.0220.0190.0000.0000.0000.0030.0000.0000.0140.0000.0150.0000.0310.0000.0000.0020.0190.0160.0240.0150.0140.0000.0080.0000.0220.0000.0180.0190.0180.0000.0000.0380.0000.000
marital_status_A0.1880.0960.0550.0540.0550.0510.0110.0150.0000.0040.0170.0040.0060.0000.0000.0160.0000.0000.0120.0031.0000.3700.4310.0660.0680.0200.0580.3720.1560.2580.2600.1070.1400.2490.3260.1570.0970.1360.0190.0400.0450.0060.2210.1160.0000.0220.0840.0610.0980.0220.0600.0680.1040.6110.2130.1140.1770.2670.0000.0040.0080.0200.0000.0000.0090.0000.000
marital_status_B0.1300.0450.0510.0560.0220.0370.0000.0000.0000.0000.0080.0300.0160.0080.0000.0000.0000.0050.0050.0000.3701.0000.0110.0000.0880.1760.1930.2210.0740.0840.2200.0790.0000.0890.1150.1010.0390.0650.1370.0050.0080.0000.0610.0280.0370.0340.0300.0900.0160.0900.0280.0650.0860.2380.0670.0480.0870.1060.0000.0060.0280.0130.0000.0000.0130.0000.000
homeowner_Homeowner0.1880.1040.0720.0800.0350.0550.0140.0000.0030.0070.0170.0150.0000.0020.0000.0270.0000.0000.0030.0000.4310.0111.0000.1830.1060.2930.0820.2040.2310.3350.1630.0000.0810.1600.1480.1320.1180.0200.1020.0380.0320.1100.1510.1840.0190.0250.0430.0930.0840.1190.0670.1850.0450.4260.2210.0030.1120.1670.0020.0160.0200.0000.0370.0000.0000.0370.002
homeowner_Probable Owner0.3040.0450.0760.0780.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0070.0660.0000.1831.0000.0000.0230.0300.0570.0320.2030.0450.0250.0400.0410.0710.0000.0580.1200.0360.0320.0150.0000.0340.0280.1800.0140.0040.0000.0140.0490.0090.0000.0270.1180.0420.0240.0380.0400.0000.0300.0080.0070.0040.0000.0180.0040.000
homeowner_Probable Renter0.1000.0860.0440.0490.0360.0260.0000.0000.0000.0030.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0880.1060.0001.0000.0110.0160.0450.0480.0520.0230.0300.0220.0220.0530.0120.0560.0270.0000.0170.0110.0190.0180.0670.0200.0020.0000.0190.0020.0000.0120.0260.0580.1130.0530.0290.0200.0220.0000.0070.0130.0020.0000.0000.0000.0000.000
homeowner_Renter0.1720.0680.0370.0330.0100.0410.0000.0080.0340.0000.0000.0000.0050.0230.0000.0030.0000.0200.0150.0060.0200.1760.2930.0230.0111.0000.2510.0370.0320.0240.0310.0130.1130.0120.0900.1110.0750.0500.1140.0490.0400.0470.0000.0000.0600.0250.0130.0140.0250.0820.0440.0370.0320.0830.0210.0310.1510.0140.0000.0100.0410.0000.0100.0000.0340.0100.000
hhcomp_1 Adult Kids0.1360.1180.1040.1180.0110.0220.0000.0090.0250.0000.0000.0070.0000.0180.0000.0000.0080.0000.0140.0100.0580.1930.0820.0300.0160.2511.0000.1660.1740.1070.0960.1030.2130.2080.3420.0000.1040.0240.0500.0160.0140.0050.0480.0220.0780.0330.0190.0050.0330.1230.0320.0770.0290.1600.0680.0800.1000.1590.0000.0250.0080.0140.0190.0000.0330.0190.000
hhcomp_2 Adults Kids0.1640.0690.1030.0980.0450.0000.0000.0000.0160.0020.0120.0000.0000.0200.0040.0000.0120.0000.0000.0120.3720.2210.2040.0570.0450.0370.1661.0000.4380.2690.2420.5510.3530.3530.8630.0280.0120.1840.1000.1140.0790.0130.1160.1080.0230.0300.0320.0120.0440.0280.0720.0800.0880.4040.5000.5590.3890.3680.0000.0050.0230.0180.0230.0120.0050.0230.000
hhcomp_2 Adults No Kids0.1310.1020.0990.1000.0180.0300.0070.0140.0250.0130.0080.0050.0130.0250.0000.0000.0120.0150.0110.0060.1560.0740.2310.0320.0480.0320.1740.4381.0000.2810.2530.2940.2230.2280.5070.0000.1040.0720.1520.0000.0190.1380.0160.0590.0150.0000.0360.0790.0380.0090.0410.0030.0270.4230.8740.2910.2120.2230.0070.0310.0240.0070.0020.0000.0420.0020.007
hhcomp_Single Female0.1610.0750.0840.0800.0170.0260.0000.0110.0010.0000.0030.0120.0080.0000.0000.0150.0000.0000.0040.0060.2580.0840.3350.2030.0520.0240.1070.2690.2811.0000.1550.1810.1370.1400.3120.0260.0000.0300.1160.1090.1600.0430.0700.1950.0720.0160.0330.0130.0470.0630.0360.0540.0710.5000.1680.1790.1300.1370.0170.0000.0140.0000.0090.0000.0040.0090.017
hhcomp_Single Male0.1910.1040.0840.0870.0290.0360.0230.0000.0210.0150.0150.0210.0000.0060.0120.0040.0000.0020.0070.0000.2600.2200.1630.0450.0230.0310.0960.2420.2530.1551.0000.1620.1230.1260.2800.0350.1350.0490.0600.0570.0510.0960.1010.0160.0920.0260.0290.0160.0150.0000.0420.0090.0000.4810.1810.1610.1170.1230.0000.0110.0000.0180.0000.0000.0220.0000.000
kid_category_10.1740.0470.0570.0540.0090.0060.0000.0000.0000.0100.0040.0000.0090.0270.0000.0210.0100.0060.0130.0030.1070.0790.0000.0250.0300.0130.1030.5510.2940.1810.1621.0000.1440.1470.5800.0310.0270.0540.0540.0800.0150.0810.0650.0250.0190.0120.0150.0400.0460.0580.1290.0700.0650.2720.2490.8930.1360.1430.0070.0000.0130.0090.0510.0210.0000.0510.007
kid_category_20.1850.1020.0720.0670.0130.0150.0000.0000.0460.0050.0000.0000.0090.0130.0000.0160.0210.0090.0180.0000.1400.0000.0810.0400.0220.1130.2130.3530.2230.1370.1230.1441.0000.1110.4410.0350.0900.0770.1050.0450.0750.0170.1030.0360.0940.0430.0260.0680.0430.0740.0260.0000.0300.2060.2560.0210.9010.1090.0000.0090.0300.0210.0130.0000.0110.0130.000
kid_category_3+0.1880.1700.0940.1070.0810.0200.0000.0180.0000.0000.0000.0000.0000.0000.0000.0040.0000.0110.0110.0220.2490.0890.1600.0410.0220.0120.2080.3530.2280.1400.1260.1470.1111.0000.4500.0290.0170.1170.0170.0460.0800.0900.1920.0780.0330.0430.0800.0110.0240.0000.0000.0280.0000.2110.2610.1450.0580.9780.0000.0240.0000.0210.0260.0000.0280.0260.000
kid_category_None/Unknown0.1620.1170.1350.1350.0500.0150.0000.0000.0290.0090.0150.0050.0000.0290.0000.0000.0190.0040.0020.0190.3260.1150.1480.0710.0530.0900.3420.8630.5070.3120.2800.5800.4410.4501.0000.0270.0650.1630.1190.1190.0840.0150.1360.0920.0640.0080.0190.0180.0230.0920.0840.0380.0690.4690.5140.5750.4180.4400.0000.0190.0290.0040.0090.0090.0250.0090.000
age_19-240.1480.0530.0510.0630.0000.0410.0000.0100.0160.0000.0000.0290.0000.0080.0000.0080.0000.0290.0000.0000.1570.1010.1320.0000.0120.1110.0000.0280.0000.0260.0350.0310.0350.0290.0271.0000.1090.1300.1540.0560.0490.0620.0590.0110.0640.0270.0060.0970.0370.0620.0000.0770.1350.0000.0200.0190.0760.0590.0000.0150.0000.0120.0110.0000.0140.0110.000
age_25-340.2400.0300.0570.0540.0140.0330.0000.0000.0080.0050.0000.0000.0240.0080.0000.0000.0070.0180.0110.0000.0970.0390.1180.0580.0560.0750.1040.0120.1040.0000.1350.0270.0900.0170.0650.1091.0000.3210.3780.1390.1230.2690.0330.0000.0110.0590.0360.0400.0690.0350.0480.0870.0600.0770.1320.0420.0590.0090.0000.0000.0330.0000.0170.0000.0280.0170.000
age_35-440.2190.0520.0490.0390.0240.0130.0000.0000.0030.0070.0060.0040.0100.0130.0000.0090.0000.0000.0060.0000.1360.0650.0200.1200.0270.0500.0240.1840.0720.0300.0490.0540.0770.1170.1630.1300.3211.0000.4520.1660.1470.1420.0550.0480.0520.0090.0470.0750.0500.1000.0000.0070.0450.1110.0470.0520.0560.1260.0000.0000.0370.0000.0000.0000.0240.0000.000
age_45-540.1990.0850.0680.0580.0130.0000.0070.0000.0110.0000.0230.0000.0100.0000.0020.0000.0020.0050.0170.0030.0190.1370.1020.0360.0000.1140.0500.1000.1520.1160.0600.0540.1050.0170.1190.1540.3780.4521.0000.1950.1740.0890.0380.0640.0250.1060.0600.0690.0280.1400.0390.1080.0250.0170.0930.0640.0810.0230.0080.0090.0180.0020.0000.0000.0070.0000.008
age_55-640.1610.0730.0350.0430.0000.0000.0000.0120.0190.0090.0000.0050.0110.0140.0000.0000.0000.0320.0000.0000.0400.0050.0380.0320.0170.0490.0160.1140.0000.1090.0570.0800.0450.0460.1190.0560.1390.1660.1951.0000.0630.0140.0170.0210.0910.0300.0200.0150.0350.0630.0790.0000.0600.0380.0730.0780.0390.0430.0000.0240.0290.0000.0070.0000.0370.0070.000
age_65+0.1790.1040.0480.0490.0000.0070.0000.0080.0000.0000.0000.0000.0250.0080.0000.0080.0000.0000.0000.0000.0450.0080.0320.0150.0110.0400.0140.0790.0190.1600.0510.0150.0750.0800.0840.0490.1230.1470.1740.0631.0000.0590.0560.2700.0690.0300.0170.0030.0300.0340.0780.0160.0440.0000.0750.0200.0740.0780.0350.0160.0110.0230.0170.0000.0000.0170.035
income_100-124K0.1980.1010.0960.1100.0170.1220.0000.0030.0000.0000.0080.0000.0000.0100.0000.0080.0000.0000.0080.0140.0060.0000.1100.0000.0190.0470.0050.0130.1380.0430.0960.0810.0170.0900.0150.0620.2690.1420.0890.0140.0591.0000.0830.0700.0900.0380.0230.0860.0380.1380.1590.1120.0690.1520.1670.0840.0270.0880.0000.0050.0240.0000.0440.0000.0160.0440.000
income_125-149K0.1860.1410.0820.0980.0290.0260.0000.0000.0160.0070.0000.0120.0000.0130.0000.0030.0040.0000.0000.0000.2210.0610.1510.0340.0180.0000.0480.1160.0160.0700.1010.0650.1030.1920.1360.0590.0330.0550.0380.0170.0560.0831.0000.0680.0870.0370.0220.0830.0370.1330.1520.1080.0660.1570.0000.0630.1170.2000.0000.0000.0210.0000.0000.0000.0150.0000.000
income_15-24K0.1210.1230.0930.0930.0000.0440.0000.0000.0000.0000.0000.0080.0350.0080.0000.0070.0000.0410.0290.0150.1160.0280.1840.0280.0670.0000.0220.1080.0590.1950.0160.0250.0360.0780.0920.0110.0000.0480.0640.0210.2700.0700.0681.0000.0730.0310.0170.0700.0310.1120.1290.0910.0550.1330.0140.0560.0300.0760.0350.0180.0220.0000.0000.0000.0000.0000.035
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income_25-34K0.1530.0530.0370.0400.0000.0380.0000.0000.0000.0010.0000.0120.0060.0100.0000.0400.0100.0060.0150.0000.0610.0900.0930.0000.0190.0140.0050.0120.0790.0130.0160.0400.0680.0110.0180.0970.0400.0750.0690.0150.0030.0860.0830.0700.0890.0380.0221.0000.0380.1370.1570.1110.0680.0540.0660.0510.0820.0060.0000.0000.0210.0140.0330.0000.0050.0330.000
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income_50-74K0.1670.0400.0980.0920.0270.0000.0000.0100.0220.0000.0150.0000.0130.0140.0000.0140.0030.0330.0070.0160.0600.0280.0670.0090.0120.0440.0320.0720.0410.0360.0420.1290.0260.0000.0840.0000.0480.0000.0390.0790.0780.1590.1520.1290.1640.0710.0430.1570.0710.2521.0000.2040.1260.0680.0100.1310.0340.0000.0000.0000.0000.0230.0220.0000.0030.0220.000
income_75-99K0.1000.0270.0430.0790.0110.0110.0000.0120.0070.0000.0000.0040.0080.0030.0000.0110.0000.0000.0230.0240.0680.0650.1850.0000.0260.0370.0770.0800.0030.0540.0090.0700.0000.0280.0380.0770.0870.0070.1080.0000.0160.1120.1080.0910.1160.0500.0300.1110.0500.1780.2041.0000.0890.0450.0050.0730.0160.0230.0000.0000.0120.0090.0230.0000.0140.0230.000
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hhsize_30.1910.0470.0530.0440.0060.0060.0000.0080.0080.0100.0040.0000.0000.0000.0010.0240.0190.0110.0120.0080.1140.0480.0030.0240.0290.0310.0800.5590.2910.1790.1610.8930.0210.1450.5750.0190.0420.0520.0640.0780.0200.0840.0630.0560.0210.0130.0150.0510.0470.0570.1310.0730.0560.2690.3331.0000.1350.1420.0070.0000.0240.0000.0510.0210.0100.0510.007
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Missing values

2023-07-09T09:42:08.970054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-09T09:42:10.274924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Hmailer_Jmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_6.0campaign_8.0campaign_13.0campaign_18.0campaign_29.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
016True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
117True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
218True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
319True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
420True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
521True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
622False0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
723True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
824False0.790.2405060.240506-0.00000000010000010100000100000100010000000000010001000000000000
925True0.790.2405060.240506-0.00000000000000010100000100000100010000000000010001000000000000
Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Hmailer_Jmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_6.0campaign_8.0campaign_13.0campaign_18.0campaign_29.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
2124134379True0.790.2405060.240506-0.00000000000000000100000100000101000000000000010001000000000000
2124234380True0.790.2405060.240506-0.00000000000000000100000100000101000000000000010001000000000000
2124334381True0.790.2405060.240506-0.00000000000000000100000100000101000000000000010001000000000000
2124434382True0.790.2405060.240506-0.00000000000000000100000100000101000000000000010001000000000000
2124534383True0.790.2405060.240506-0.00000000000000000100000100000101000000000000010001000000000000
2124634384True0.790.2405060.240506-0.00000000000000000100000100000101000000000000010001000000000000
2124734385True2.690.0743490.074349-0.00000000000000000100000100000101000000000000010001000000100100
2124834386True2.790.1039430.103943-0.00000000000000000000001000100001000000000000000100100000000000
2124934387True2.790.1039430.103943-0.00000000000000000000001000100001000000000000000100100000000000
2125034388True3.990.2481200.248120-0.00000000000000000000001000100001000000000000000100100000000000